In this work, we propose and test a method to expedite Global Sensitivity Analysis (GSA) in
the context of shape optimisation of free-form shapes. To leverage the computational burden …

Large-scale, high-quality data sets are central to the development of advanced machine
learning techniques that increase the effectiveness of existing optimization methods or even …

Generative AI models have made significant progress in automating the creation of 3D
shapes, which has the potential to transform car design. In engineering design and …

The choice of design representations, as of search operators, is central to the performance
of evolutionary optimization algorithms, in particular, for multitask problems. The multitask …

In automotive digital development, engineers utilize multiple virtual prototyping tools to
design and assess the performance of 3D shapes. However, accurate performance …

In order to reduce the dimensionality of 3D point cloud representations, autoencoder
architectures generate increasingly abstract, compressed features of the input data …

Variational autoencoders (VAEs) play an important role in high-dimensional data generation
based on their ability to fuse the stochastic data representation with the power of recent …

This study introduces DrivAerNet, a large-scale high-fidelity CFD dataset of 3D industry-
standard car shapes, and RegDGCNN, a dynamic graph convolutional neural network …

During each cycle of automotive development, large amounts of geometric data are
generated as results of design studies and simulation tasks. Discovering hidden knowledge …

Methods for learning on 3D point clouds became ubiquitous due to the popularization of 3D
scanning technology and advances of machine learning techniques. Among these methods …